Create mswc.py

mswc.py

@@ -0,0 +1,166 @@
+# coding=utf-8
+
+"""MSWC keyword spotting classification dataset."""
+
+
+import os
+import textwrap
+import datasets
+import itertools
+import typing as tp
+from pathlib import Path
+
+from ._mswc import (
+    TRAIN_ENG, VALIDATION_ENG, TEST_ENG, 
+    TRAIN_SPA, VALIDATION_SPA, TEST_SPA, 
+    TRAIN_IND, VALIDATION_IND, TEST_IND, 
+)
+
+FOLDER_IN_ARCHIVE = "genres"
+SAMPLE_RATE = 16_000
+
+_ENG_FILENAME = 'eng-kw-archive.tar.gz'
+_SPA_FILENAME = 'spa-kw-archive.tar.gz'
+_IND_FILENAME = 'ind-kw-archive.tar.gz'
+
+CLASS_ENG = set([fileid.split('_')[0] for fileid in TRAIN_ENG])
+CLASS_SPA = set([fileid.split('_')[0] for fileid in TRAIN_SPA])
+CLASS_IND = set([fileid.split('_')[0] for fileid in TRAIN_IND])
+
+
+class MswcConfig(datasets.BuilderConfig):
+    """BuilderConfig for MSWC."""
+    
+    def __init__(self, features, **kwargs):
+        super(MswcConfig, self).__init__(version=datasets.Version("0.0.1", ""), **kwargs)
+        self.features = features
+
+
+class MSWC(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        MswcConfig(
+            features=datasets.Features(
+                {
+                    "file": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=SAMPLE_RATE),
+                    "keyword": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names=CLASS_ENG), 
+                }
+            ),
+            name="english", 
+            description=textwrap.dedent(
+                """\
+                Keyword spotting classifies each audio for its keywords as a multi-class
+                classification, where keywords are in the same pre-defined set for both training and testing. 
+                The evaluation metric is accuracy (ACC).
+                """
+            ), 
+        ), 
+        MswcConfig(
+            features=datasets.Features(
+                {
+                    "file": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=SAMPLE_RATE),
+                    "keyword": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names=CLASS_SPA), 
+                }
+            ),
+            name="spanish", 
+            description=textwrap.dedent(
+                """\
+                Keyword spotting classifies each audio for its keywords as a multi-class
+                classification, where keywords are in the same pre-defined set for both training and testing. 
+                The evaluation metric is accuracy (ACC).
+                """
+            ), 
+        ), 
+        MswcConfig(
+            features=datasets.Features(
+                {
+                    "file": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=SAMPLE_RATE),
+                    "keyword": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names=CLASS_IND), 
+                }
+            ),
+            name="indian", 
+            description=textwrap.dedent(
+                """\
+                Keyword spotting classifies each audio for its keywords as a multi-class
+                classification, where keywords are in the same pre-defined set for both training and testing. 
+                The evaluation metric is accuracy (ACC).
+                """
+            ), 
+        ), 
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description="",
+            features=self.config.features,
+            supervised_keys=None,
+            homepage="",
+            citation="",
+            task_templates=None,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        if self.config.name == "english":
+            archive_path = dl_manager.extract(_ENG_FILENAME)
+        elif self.config.name == "spanish":
+            archive_path = dl_manager.extract(_SPA_FILENAME)
+        elif self.config.name == "indian":
+            archive_path = dl_manager.extract(_IND_FILENAME)
+            
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"archive_path": archive_path, "split": "train"}
+            ), 
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION, gen_kwargs={"archive_path": archive_path, "split": "validation"}
+            ), 
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path, "split": "test"}
+            ), 
+        ]
+
+    def _generate_examples(self, archive_path, split=None):
+        if self.config.name == 'english':
+            extensions = ['.wav']
+            _, _walker = fast_scandir(archive_path, extensions, recursive=True)
+            if subset == 'train':
+                _walker = [fileid for fileid in _walker if Path(fileid).stem in TRAIN_ENG]
+            elif subset == 'validation':
+                _walker = [fileid for fileid in _walker if Path(fileid).stem in VALIDATION_ENG]
+            elif subset == 'test':
+                _walker = [fileid for fileid in _walker if Path(fileid).stem in TEST_ENG]
+        elif self.config.name == 'spanish':
+            extensions = ['.wav']
+            _, _walker = fast_scandir(archive_path, extensions, recursive=True)
+            if subset == 'train':
+                _walker = [fileid for fileid in _walker if Path(fileid).stem in TRAIN_ENG]
+            elif subset == 'validation':
+                _walker = [fileid for fileid in _walker if Path(fileid).stem in VALIDATION_ENG]
+            elif subset == 'test':
+                _walker = [fileid for fileid in _walker if Path(fileid).stem in TEST_ENG]
+        elif self.config.name == 'indian':
+            extensions = ['.wav']
+            _, _walker = fast_scandir(archive_path, extensions, recursive=True)
+            if subset == 'train':
+                _walker = [fileid for fileid in _walker if Path(fileid).stem in TRAIN_ENG]
+            elif subset == 'validation':
+                _walker = [fileid for fileid in _walker if Path(fileid).stem in VALIDATION_ENG]
+            elif subset == 'test':
+                _walker = [fileid for fileid in _walker if Path(fileid).stem in TEST_ENG]
+
+        for guid, audio_path in enumerate(_walker):
+            yield guid, {
+                "id": str(guid),
+                "file": audio_path, 
+                "audio": audio_path, 
+                "keyword": Path(audio_path).stem.split('_')[0], 
+                "label": Path(audio_path).stem.split('_')[0], 
+            }